This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Most microbial communities in natural and clinical settings are spatially structured, appearing as surface-associated populations such as biofilms. This structure profoundly affects the ecological and evolutionary forces that shape the community. This is particularly important in the battle against ever-evolving human pathogens that have ecological and evolutionary time scales much shorter than human time scales. Despite this, very little is known about the specific effects of spatial heterogeneity on the adaptive evolution of microbial pathogens. Thus, a joint experimental and theoretical investigation is proposed to uncover fundamental details about the role of spatial structure in driving evolutionary and ecological dynamics of viruses. A simple experimental model system will provide a framework for addressing these issues. This experimental system will be used to study the molecular adaptive evolution of bacteriophages infecting their bacterial hosts on agar plates under changing environmental conditions. In conjunction with these experiments, new spatially explicit mathematical models (stochastic cellular automata) will be developed and fit to empirical observations with the aims of predicting the behavior of the microbial systems, uncovering the underlying biological mechanisms that are most important in the adaptive evolution of viruses, and generating new hypotheses. This project will focus on the impact of spatial structure on adaptive evolution to a single change in environment and will generate preliminary data for an independent R01 grant application that addresses adaptive evolution in the presence of fluctuating environmental conditions.